Dynamic Prediction Research of Silicon Content in Hot Metal Driven by Big Data in Blast Furnace Smelting Process under Hadoop Cloud Platform

In order to explore a dynamic prediction model with good generalization performance of the content of [Si] in molten iron, an improved SVM algorithm is proposed to enhance its practicability in the big data sample set of the smelting process. Firstly, we propose a parallelization scheme to design an...

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Vydáno v:	Complexity (New York, N.Y.) Ročník 2018; číslo 2018; s. 1 - 16
Hlavní autoři:	Liu, Wei-Xing, Yang, Xiao-Lei, Li, Jie, Han, Yang, Zhang, Yu-Zhu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cairo, Egypt Hindawi Publishing Corporation 01.01.2018 Hindawi John Wiley & Sons, Inc Wiley
Témata:	Algorithms Artificial intelligence Big Data Cloud computing Computers Covariance matrix Data management Decomposition Distance learning Efficiency Explosions Fault diagnosis Forecasting Historical metallurgy Hot blast Iron Iron and steel plants Ironmaking Iterative methods Mathematical models Parallel processing Process controls Propagation Reaction mechanisms Silicon Smelting Software Steel industry Teaching methods
ISSN:	1076-2787, 1099-0526
On-line přístup:	Získat plný text
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